Abstract: Vulnerability detection and safety of smart contracts are of paramount
importance because of their immutable nature. Symbolic tools like OYENTE and
MAIAN are typically used for vulnerability prediction in smart contracts. As
these tools are computationally expensive, they are typically used to detect
vulnerabilities until some predefined invocation depth. These tools require
more search time as the invocation depth increases. Since the number of smart
contracts is increasing exponentially, it is difficult to analyze the contracts
using these traditional tools. Recently a machine learning technique called
Long Short Term Memory (LSTM) has been used for binary classification, i.e., to
predict whether a smart contract is vulnerable or not. This technique requires
nearly constant search time as the invocation depth increases. In the present
article, we have shown a multi-class classification, where we classify a smart
contract in Suicidal, Prodigal, Greedy, or Normal categories. We used Average
Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), which is a variant
of LSTM, to perform classification. We reduced the class imbalance (a large
number of normal contracts as compared to other categories) by considering only
the distinct opcode combination for normal contracts. We have achieved a
weighted average Fbeta score of 90.0%. Hence, such techniques can be used to
analyze a large number of smart contracts and help to improve the security of
these contracts.